Development and implementation of the SUM breast cancer cell line functional genomics knowledge base.

Cancer genomics Cell signalling

Journal

NPJ breast cancer
ISSN: 2374-4677
Titre abrégé: NPJ Breast Cancer
Pays: United States
ID NLM: 101674891

Informations de publication

Date de publication:
2020
Historique:
received: 25 11 2019
accepted: 23 06 2020
entrez: 28 7 2020
pubmed: 28 7 2020
medline: 28 7 2020
Statut: epublish

Résumé

Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines.

Identifiants

pubmed: 32715085
doi: 10.1038/s41523-020-0173-z
pii: 173
pmc: PMC7374090
doi:

Types de publication

Journal Article

Langues

eng

Pagination

30

Informations de copyright

© The Author(s) 2020.

Déclaration de conflit d'intérêts

Competing interestsThe authors declare no competing interests.

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Auteurs

Stephen P Ethier (SP)

Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.

Stephen T Guest (ST)

Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.
Present Address: Department of Biomedical Informatics, University of Michigan Medical School, Ann Arbor, MI USA.

Elizabeth Garrett-Mayer (E)

Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.
Present Address: American Society for Clinical Oncology, Charleston, SC USA.

Kent Armeson (K)

Biostatistics Core, Hollings Cancer Center, Charleston, SC USA.

Robert C Wilson (RC)

Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.

Kathryn Duchinski (K)

Department of Computer Science, The College of Charleston, Charleston, SC USA.
Present Address: Program in Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA USA.

Daniel Couch (D)

Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.

Joe W Gray (JW)

Department of Biomedical Engineering, Oregon Health and Sciences University, Portland, OR USA.

Christiana Kappler (C)

Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.

Classifications MeSH